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Through the lens of market participants' objective to minimize counterparty risk, we provide an explanation for the reluctance to clear derivative trades in the absence of a central clearing obligation. We develop a comprehensive understanding of the benefits and potential pitfalls with respect to a single market participant's counterparty risk exposure when moving from a bilateral to a clearing architecture for derivative markets. Previous studies suggest that central clearing is beneficial for single market participants in the presence of a sufficiently large number of clearing members. We show that three elements can render central clearing harmful for a market participant's counterparty risk exposure regardless of the number of its counterparties: 1) correlation across and within derivative classes (i.e., systematic risk), 2) collateralization of derivative claims, and 3) loss sharing among clearing members. Our results have substantial implications for the design of derivatives markets, and highlight that recent central clearing reforms might not incentivize market participants to clear derivatives.
We study whether the presence of low-latency traders (including high-frequency traders (HFTs)) in the pre-opening period contributes to market quality, defined by price discovery and liquidity provision, in the opening auction. We use a unique dataset from the Tokyo Stock Exchange (TSE) based on server-IDs and find that HFTs dynamically alter their presence in different stocks and on different days. In spite of the lack of immediate execution, about one quarter of HFTs participate in the pre-opening period, and contribute significantly to market quality in the pre-opening period, the opening auction that ensues and the continuous trading period. Their contribution is largely different from that of the other HFTs during the continuous period.
We show that High Frequency Traders (HFTs) are not beneficial to the stock market during flash crashes. They actually consume liquidity when it is most needed, even when they are rewarded by the exchange to provide immediacy. The behavior of HFTs exacerbate the transient price impact, unrelated to fundamentals, typically observed during a flash crash. Slow traders provide liquidity instead of HFTs, taking advantage of the discounted price. We thus uncover a trade-o↵ between the greater liquidity and efficiency provided by HFTs in normal times, and the disruptive consequences of their trading activity during distressed times.
We study how the Eurosystem Collateral Framework for corporate bonds helps the European Central Bank (ECB) fulfill its policy mandate. Using the ECBs eligibility list, we identify the first inclusion date of both bonds and issuers. We find that due to the increased supply and demand for pledgeable collateral following eligibility, (i) securities lending market trading activity increases, (ii) eligible bonds have lower yields, and (iii) the liquidity of newly-issued bonds declines, whereas the liquidity of older bonds is unaffected/improves. Corporate bond lending relaxes the constraint of limited collateral supply, thereby making the market more cohesive and complete. Following eligibility, bond-issuing firms reduce bank debt and expand corporate bond issuance, thus increasing overall debt size and extending maturity.
We focus on the role of social media as a high-frequency, unfiltered mass information transmission channel and how its use for government communication affects the aggregate stock markets. To measure this effect, we concentrate on one of the most prominent Twitter users, the 45th President of the United States, Donald J. Trump. We analyze around 1,400 of his tweets related to the US economy and classify them by topic and textual sentiment using machine learning algorithms. We investigate whether the tweets contain relevant information for financial markets, i.e. whether they affect market returns, volatility, and trading volumes. Using high-frequency data, we find that Trump’s tweets are most often a reaction to pre-existing market trends and therefore do not provide material new information that would influence prices or trading. We show that past market information can help predict Trump’s decision to tweet about the economy.
The impact of network connectivity on factor exposures, asset pricing and portfolio diversification
(2017)
This paper extends the classic factor-based asset pricing model by including network linkages in linear factor models. We assume that the network linkages are exogenously provided. This extension of the model allows a better understanding of the causes of systematic risk and shows that (i) network exposures act as an inflating factor for systematic exposure to common factors and (ii) the power of diversification is reduced by the presence of network connections. Moreover, we show that in the presence of network links a misspecified traditional linear factor model presents residuals that are correlated and heteroskedastic. We support our claims with an extensive simulation experiment.
An important assumption underlying the designation of some insurers as systemically important is that their overlapping portfolio holdings can result in common selling. We measure the overlap in holdings using cosine similarity, and show that insurers with more similar portfolios have larger subsequent common sales. This relationship can be magnified for some insurers when they are regulatory capital constrained or markets are under stress. When faced with an exogenous liquidity shock, insurers with greater portfolio similarity have even larger common sales that impact prices. Our measure can be used by regulators to predict which institutions may contribute most to financial instability through the asset liquidation channel of risk transmission.
We employ a representative sample of 80,972 Italian firms to forecast the drop in profits and the equity shortfall triggered by the COVID-19 lockdown. A 3-month lockdown generates an aggregate yearly drop in profits of about 10% of GDP, and 17% of sample firms, which employ 8.8% of the sample’s employees, become financially distressed. Distress is more frequent for small and medium-sized enterprises, for firms with high pre-COVID-19 leverage, and for firms belonging to the Manufacturing and Wholesale Trading sectors. Listed companies are less likely to enter distress, whereas the correlation between distress rates and family firm ownership is unclear.
(JEL G01, G32, G33)